Methods and apparatus for serving relevant advertisements

ABSTRACT

The relevance of advertisements to a user&#39;s interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/816,205, titled “METHODS AND APPARATUS FOR SERVING RELEVANTADVERTISEMENTS,” filed on Aug. 3, 2015 and listing Jeffrey A. Dean,Georges R. Harik and Paul Buchheit as the inventors, which is acontinuation application of U.S. patent application Ser. No. 14/249,146,titled “METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS,”filed on Apr. 9, 2014 and listing Jeffrey A. Dean, Georges R. Harik andPaul Buchheit as the inventors, which is a continuation of U.S. patentapplication Ser. No. 13/420,801, titled “METHODS AND APPARATUS FORSERVING RELEVANT ADVERTISEMENTS,” filed on Mar. 15, 2012 and listingJeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors,which is a continuation of U.S. patent application Ser. No. 12/729,054(now U.S. Pat. No. 8,171,034), titled “METHODS AND APPARATUS FOR SERVINGRELEVANT ADVERTISEMENTS,” filed on Mar. 22, 2010 and listing Jeffrey A.Dean, Georges R. Harik and Paul Buchheit as the inventors, which is acontinuation of U.S. patent application Ser. No. 10/314,427 (now U.S.Pat. No. 7,716,161), titled “METHODS AND APPARATUS FOR SERVING RELEVANTADVERTISEMENTS,” filed on Dec. 6, 2002 and listing Jeffrey A. Dean,Georges R. Harik and Paul Buchheit as the inventors, which claimedbenefit under 35 U.S.C. § 119(e)(1), to the filing date of provisionalpatent application Ser. No. 60/413,536, titled “METHODS AND APPARATUSFOR SERVING RELEVANT ADVERTISEMENTS”, filed on Sep. 24, 2002 and listingJeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors,for any inventions disclosed in the manner provided by 35 U.S.C. § 112,¶1. Each of the foregoing provisional and utility applications isexpressly incorporated herein by reference.

BACKGROUND OF THE INVENTION

A. Field of the Invention

The present invention relates generally to advertising and, moreparticularly, to serving relevant advertisements by comparingadvertisers' targeting criteria to the content of media on which theadvertisements are to be published.

B. Description of Related Art

Advertising using traditional media, such as television, radio,newspapers and magazines, is well known. Advertisers have used thesetypes of media to reach a large audience with their advertisements(“ads”). To reach a more responsive audience, advertisers have useddemographic studies. For example, advertisers may use broadcast eventssuch as football games to advertise beer and action movies to a youngermale audience. However, even with demographic studies and entirelyreasonable assumptions about the typical audience of various mediaoutlets, advertisers recognize that much of their ad budget is simplywasted because the target audience is not interested in the ad they arereceiving.

Interactive media, such as the Internet, has the potential for bettertargeting of advertisements. For example, some websites provide aninformation search functionality that is based on query keywords enteredby the user seeking information. This user query can be used as anindicator of the type of information of interest to the user. Bycomparing the user query to a list of keywords specified by anadvertiser, it is possible to provide some form of targetedadvertisements to these search service users. An example of such asystem is the Adwords system offered by Google, Inc.

While systems such as Adwords have provided advertisers the ability tobetter target ads, their effectiveness is limited to sites where a userenters a search query to indicate their topic of interest. Most webpages, however, do not offer search functionality and for these pages itis difficult for advertisers to target their ads. As a result, often,the ads on non-search pages are of little value to the viewer of thepage and are therefore viewed more as an annoyance than a source ofuseful information. Not surprisingly, these ads typically provide theadvertiser with a lower return on investment than search-based ads,which are more targeted.

It would be useful, therefore, to have methods and apparatus forproviding relevant ads for situations where a document is provided to anend user, but not in response to an express indication of a topic ofinterest by the end user (e.g., not responsive to the end usersubmitting a search query).

SUMMARY OF THE INVENTION

Systems and methods consistent with the present invention address thisand other needs by identifying targeting information for anadvertisement, analyzing the content of a target document to identify alist of one or more topics for the target document, comparing thetargeting information to the list of topics to determine if a matchexists, and determining that the advertisement is relevant to the targetdocument if the match exists.

Additional aspects of the present invention are directed to computersystems and to computer-readable media having features relating to theforegoing aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an embodiment of the inventionand, together with the description, explain the invention. In thedrawings,

FIG. 1 is a diagram illustrating an environment within which theinvention may be implemented;

FIG. 2 is a diagram functionally illustrating an advertising systemconsistent with the invention;

FIG. 3 is a diagram illustrating apparatus with which the invention maybe implemented;

FIG. 4 is a flow diagram of an exemplary method for providing relevantadvertisements, consistent with the present invention; and

FIG. 5 is a sample target document.

DETAILED DESCRIPTION

The following detailed description of the invention refers to theaccompanying drawings. The detailed description does not limit theinvention. Instead, the scope of the invention is defined by theappended claims and equivalents.

The present invention involves methods and apparatus for determiningadvertisements that are relevant to a given document. In oneimplementation, the document is a web page and the advertisements areelectronic files that are capable of being rendered on that web page. Aset, such as a list, of topics corresponding to the web page isgenerated by analyzing the content of the web page. There are a varietyof techniques by which this may be performed, one of which is bycomputing a term vector for the web page and selecting the top N termsfrom that vector. The list of topics is compared to target informationassociated with the advertisements (e.g., keywords specified for theadvertisements) to determine which of the advertisements are relevant tothe web page. Some or all of these relevant advertisements may then beassociated with the web page so that they may be rendered (e.g.,displayed) with the web page.

Those skilled in the art will recognize that many other implementationsare possible, consistent with the present invention.

A. Environment and Architecture

FIG. 1 is a diagram illustrating an environment within which theinvention may be implemented. The environment includes an advertiser110, an advertising system 120, an advertisement consumer 130, and anadvertising target 140.

Advertiser 110 may be the party that directly sells the goods orservices being advertised (e.g., Amazon.com) or an agent authorized toact on the advertiser's behalf. The advertisement desired by advertiser110 may exist in a variety of forms ranging from standard printadvertisements, online advertisements, audio advertisements,audio/visual advertisements, or any other type of sensory messagedesired.

Advertising system 120 interfaces with both the advertiser 110 and theadvertisement consumer 130. It may perform a variety of functions, asexplained in more detail below in reference to FIG. 2. This inventionmay be used with such an advertising system 120.

Advertisement consumer 130 is the entity that will issue a request foradvertisements to advertising system 120, obtain the advertisements fromadvertising system 120, and present the advertisement to the advertisingtarget 140. Typically, the advertisement consumer is the entity thatprovides the content with which the advertisement is to be associated.In one implementation, the advertising consumer 130 is a search engine,such as that employed by Google, Inc. at www.google.com.

Advertising target 140 is the individual (or set of individuals) whoultimately receive the advertisement. In the case of visualadvertisements, for example, the advertisement target 140 is the personwho views the advertisement.

FIG. 2 is a diagram functionally illustrating an advertising systemconsistent with the invention. The system includes an ad campaign entryand management component 210, a tools component 220, a billing component230, one or more databases 240, an ad consumer interface component 250,an ad selection component 260, an ad ordering component 270, an adserving component 280, and a statistics engine component 290. If thepresent invention is to be used with such an advertising system, it willprimarily concern ad selection component 260. To help understand theinvention, other components of the advertising system will be explainedbelow. Furthermore, although FIG. 2 shows a particular arrangement ofcomponents constituting advertisement system 120, those skilled in theart will recognize that not all components need be arranged as shown,not all components are required, and that other components may be addedto, or replace, those shown.

Ad entry and management component 210 is the component by which theadvertiser enters information required for an advertising campaign andmanages the campaign. An ad campaign contains one or more advertisementsthat are related in some manner. For example, the Ford Motor Company mayhave an ad campaign for zero percent financing, which could contain aseries of advertisements related to that topic. Among the other thingsthat could be provided by an advertiser through ad entry and managementcomponent 210 are the following: one or more advertising creatives(simply referred to as “ads” or “advertisements”), one or more set ofkeywords or topics associated with those creatives (which may be used astargeting information for the ads), geographic targeting information, avalue indication for the advertisement, start date, end date, etc. Thedata required for, or obtained by, ad entry and management component 210resides in one of the databases 240.

Tools component 220 contains a variety of tools designed to help theadvertiser 110 create, monitor, and manage its campaigns. For example,tools component 220 may contain a tool for helping advertiser 110estimate the number of impressions an ad will receive for a particularkeyword or topic. Similarly, tools component 220 may be used to helpadvertiser 110 generate a list of keywords or topics for a givenadvertisement, or to generate additional keywords or topics based onrepresentative ones supplied by advertiser 110. Other possible tools maybe provided as well. Depending on the nature of the tool, one or moredatabases 240 may be used to gather or store information.

Billing component 230 helps perform billing-related functions. Forexample, billing component 230 generates invoices for a particularadvertiser 110 or ad campaign. In addition, billing component 230 may beused by advertiser 110 to monitor the amount being expended for itsvarious campaigns. The data required for, or obtained by, billingcomponent 230 resides in a database 240.

Databases 240 contain a variety of data used by advertising system 120.In addition to the information mentioned above in reference to ad entryand management system 210, databases 240 may contain statisticalinformation about what ads have been shown, how often they have beenshown, the number of times they have been selected, who has selectedthose ads, how often display of the ad has led to consummation of atransaction, etc. Although the databases 240 are shown in FIG. 2 as oneunit, one of ordinary skill in the art will recognize that multipledatabases may be employed for gathering and storing information used inadvertising system 120.

Ad consumer interface 250 is a component that interfaces with adconsumer 130 to obtain or send information. For example, ad consumer 130may send a request for one or more advertisements to ad consumerinterface 250. The request may include information such as the siterequesting the advertisement, any information available to aid inselecting the advertisement, the number of ads requested, etc. Inresponse, ad consumer interface 250 may provide one or moreadvertisements to ad consumer 130. In addition, ad consumer 130 may sendinformation about the performance of the advertisement back to the adsystem via the ad consumer interface 250. This may include, for example,the statistical information described above in reference to a database240. The data required for, or obtained by, ad consumer interfacecomponent 250 resides in a database 240.

Ad selection component 260 receives a request for a specified number ofadvertisements, coupled with information to help select the appropriateadvertisements. This information may include, for example, a searchquery specified by an end user. Alternatively, or in addition, asdescribed in more detail below, this information may include datarelated to the content of the page for which the advertisements arebeing requested.

Ad ordering component 270 receives a list of relevant ads from adselection component 260 and determines a preference order in which theyshould be rendered to an end user. For example, relevant ads may beordered based on the value indication associated with each ad. Theseordered ads may be provided to an ad serving component 280.

Ad serving component 280 receives an ordered list of ads from adordering component 270, and formats that list into a manner suitable forpresenting to ad consumer 130. This may involve, for example, renderingthe ads into hypertext markup language (HTML), into a proprietary dataformat, etc.

Statistics engine 290 contains information pertaining to the selectionand performance of advertisements. For example, statistics engine 290may log the information provided by ad consumer 130 as part of an adrequest, the ads selected for that request by ad selection component260, the order selected by ad ordering component 270, and thepresentation of the ads by ad serving component 280. In addition,statistics engine 290 may log information about what happens with theadvertisement once it has been provided to ad consumer 130. Thisincludes information such as on what location the ad was provided, whatthe response was to the advertisement, what the effect was of theadvertisement, etc.

FIG. 3 is a diagram illustrating an architecture in which the presentinvention may be implemented. The architecture includes multiple clientdevices 302, a server device 310, and a network 301, which may be, forexample, the Internet. Client devices 302 each include acomputer-readable medium 309, such as random access memory, coupled to aprocessor 308. Processor 308 executes program instructions stored inmemory 309. Client devices 302 may also include a number of additionalexternal or internal devices, such as, without limitation, a mouse, aCD-ROM, a keyboard, and a display. Thus, as will be appreciated by thoseskilled in the art, the client devices may be personal computers,personal digital assistances, mobile phones, content players, etc.

Through client devices 302, requestors 305 can communicate over network301 with each other and with other systems and devices coupled tonetwork 301, such as server device 310. Requestors 305 may, for example,be advertisers 110, advertisement consumer 130, or advertising target140.

Similar to client devices 302, server device 310 may include a processor311 coupled to a computer readable memory 312. Server device 310 mayadditionally include a secondary storage element, such as a database240.

Client processors 308 and server processor 311 can be any of a number ofwell known micro-processors, such as processors from Intel Corporation,of Santa Clara, Calif. In general, client device 302 may be any type ofcomputing platform connected to a network and that interacts withapplication programs, such as a digital assistant or a “smart” cellulartelephone or pager. Server 310, although depicted as a single computersystem, may be implemented as a network of computer processors.

Memory 312 may contain a number of programs, such as the componentsdescribed above in reference to FIG. 2.

B. Operation

FIG. 4 is a flow diagram of an exemplary method for determining if anadvertisement is relevant to a document, consistent with the presentinvention. As used herein, the term “document” includes any type ofpaper or electronic document or file, including audio, video, image,text, etc. That is, as will be appreciated by one skilled in the art, a“document” as used in the specification is any machine-readable andmachine-storable work product. A document may be a file, a combinationof files, one or more files with embedded links to other files, etc. Forthe sake of illustration, it may be understood that the processdescribed herein takes place as part of the ad selection component 260,although those skilled in the art will recognize that it need not takeplace in that component alone.

The exemplary method is not limited by the order shown in the flowdiagram. The process identifies targeting information for anadvertisement. (Stage 410). The targeting information may be in the formof a list of keywords or phrases associated with the advertisement(e.g., “honda”, “honda cars”, “cars”, etc.), as provided by advertiser110 through ad campaign entry and management component 210.Alternatively, or in addition, the targeting information may bedetermined algorithmically, based on the content of the advertisement,the goods or services being advertised, the targeting of other relatedadvertisements, etc. For example, if the content of the advertisementincludes “Buy honda cars at the lowest prices of the year!”, the terms“honda” or “honda cars” may be extracted from that content. Thetargeting information may also include other demographic information,such as geographic location, affluence, etc. Thus, the targetinginformation is simply some information from which a topic may bederived.

Next, the target document (i.e., the document corresponding to which arelevant advertisement is requested) is analyzed to identify a topiccorresponding to that target document. (Stage 420). The target documentmay be stored on a database 240 or may be provided by ad consumer 130via ad consumer interface component 250. There are numerous ways inwhich the target document may be analyzed to identify this topic, asdescribed below in reference to FIG. 5 and related text.

The targeting information identified in stage 410 is compared to the oneor more topics identified in stage 420 to determine if a match exists.(Stage 430). A “match” need not be an exact match. Instead, a match isan indication of a relatively high degree of similarly, and/or apredetermined (e.g., absolute) degree of similarity. If a match exists,the advertisement is determined to be relevant to the target document(stage 440) and may be provided to ad ordering component 270, foreventual provision to ad consumer 130 via ad consumer interfacecomponent 250.

Those skilled in the art will also recognize that the functionsdescribed in each stage are illustrative only, and are not intended tobe limiting.

One way to identify a topic corresponding to the target document is byanalyzing some or all text within the target document, which shall beillustrated in reference to FIG. 5. FIG. 5 shows a sample document,entitled “Travels in Italy”, which contains a collection oftravel-related information pertaining to Italy. The document textcontains the term “restaurant” (appearing 20 times), “chianti”(appearing 10 times), and “the” (appearing 100 times). It could bedetermined that one or more of each term (word or phrase) that appearsin the title of the target document corresponds to a topic of the targetdocument. On this basis, the topics for this document may be “travels”,“in”, and/or “italy.”

Alternatively, it could be determined that one or more of each term thatappears in the body of the target document corresponds to a topic of thetarget document. In the simplest case, each term within the targetdocument would be identified as a topic. A slightly more complexapproach would be to identify a term as a topic if it appears in thetarget document more than N times, such as N=2 (and indeed such athreshold-based approach could be used whenever terms within text arebeing analyzed). Even more complex analysis could be performed, such asby using a term vector for the target document, which assigns weights toeach term. For example, terms that appear frequently in the targetdocument may be assigned a relatively higher weight than those thatappear less frequently. And so the term “the” would have a higher weightthan “restaurant”, which would have a higher weight than “chianti”.

In addition, the weighting could be adjusted to give higher weight toterms that appear less frequently in a collection, such as a collectionto which the document belongs or the general collection of documents.For example, the term “chianti” does not appear very commonly across thegeneral collection of documents and so its weight may be boosted.Conversely, the term “the” appears so frequently across a collection ofdocuments that its weight may be reduced or eliminated altogether.

In any situation where terms within text are assigned weights or scores,those resulting scores may be used to determine which terms will beidentified as topics for the target document. For example, it may bedetermined that only the top scoring term would constitute a topic forthe target document. Alternatively, or in addition, it may be determinedthat the top Z terms (or a subset thereof) will constitute topics forthe target document, with Z being some defined number. Alternatively, orin addition, it may be determined that terms having a score that exceedsY (or a subset thereof) will constitute topics for the target document,with Y being some defined number. Thus, as one skilled in the art willappreciate, topics may be determined based on absolute and/or relativecriteria.

Alternatively, or in addition to using text or other information withinthe target document, meta-information associated with the targetdocument may be used. For example, a reference to the target document byanother document may contain a brief description of the target document.Assume a document called “Entertainment” that contains a reference tothe target document and describes it as “For a description ofrestaurants and wine in Italy, see ‘Travels in Italy’.” In the contextof a web page, this is often described as anchor text. One or more suchbrief descriptions may be used to revise (figuratively) the targetdocument by supplementing or replacing some or all of its content withthe brief descriptions. So, for example, the topic could be identifiedfrom the combination of the target document's title and the briefdescriptions of the target document.

Alternatively, or in addition to the brief descriptions from thesereferences, the references themselves may be used. For example, areference from another document to the target document may be used as anindication that the two documents are similar. Alternatively, or inaddition, a reference from the target document to another document maybe used as an indication that the two documents are similar. So areference between the “Entertainment” document and the “Travels inItaly” document may indicate that the two are related. In the context ofweb pages, these references occur in the form of links from one web pageto another. On this basis, the content (or meta-information) of theother document may be used to revise (figuratively) the target documentby supplementing or replacing its content with that of the otherdocument. The revised target document's content may then be analyzedusing the techniques described above to identify one or more topics.

Alternatively, or addition to using the content (including perhapsmeta-data) associated with a target document, other techniques may beused to identify one or more topics for the target document. Forexample, the top N queries (or subset thereof) that result in areference to the target document could be determined to constitute atopic for the target document, with N being some defined number. Thesemay be, for example, text queries in a search engine that yield a resultthat links to the target document or web page. Alternatively, or inaddition, the content of other similar documents (e.g., in the samecollection as the target document, in the same category as the targetdocument, etc.) may be used to revise (figuratively) the content of thetarget document. Any of the techniques described above may then be usedto analyze the target document to identify one or more topics. In thecontext of web pages, this may be other web pages that are stored withina subdirectory of related pages on the same host as the target web page.Alternatively, or in addition, any technique for classifying the targetdocument into a set of one or more topics or categories may be used.Even the search query history of one or more users who visit the targetdocument (or target web page) may be used to identify a topic for thetarget document or web page, on the theory that a visit to the targetdocument that is temporally proximate to that search query historyindicates that the user thought the concepts were related. For example,if a user searched for “italian wine” and then soon afterwards visitedthe “Travels in Italy” document, the content of that prior search couldbe used to determine that “italian” and/or “wine” are potential topicsfor the “Travels in Italy” document.

Using one or more of the various techniques described above, or othertechniques, one or more topics may be identified for the targetdocument. Once these topics have been identified, a variety oftechniques may be used to determine other topics that are related tothose identified topics. For example, a thesaurus could be used todetermine other topics (e.g., synonyms) that are closely related to theidentified topics or that are conceptually similar to the identifiedtopics.

For the sake of clarity, the foregoing references to “revising” thetarget document are a figurative aid in understanding the use ofadditional information that is not literally within the target document.Those skilled in the art will recognize that the target document neednot be actually revised to make use of this additional information.

C. Conclusion

The foregoing description of preferred embodiments of the presentinvention provides illustration and description, but is not intended tobe exhaustive or to limit the invention to the precise form disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the invention.

The scope of the invention is defined by the claims and theirequivalents.

What is claimed:
 1. A computer implemented method comprising: obtaininga set of rules that specify weights for each term of a plurality ofterms extracted from a resource, with a weight of a term being afunction of a frequency of the term in the resource; extracting aplurality of terms from a target resource; generating, based on the setof rules, a term vector that represents weights for the plurality ofterms that appear within the target resource; determining that one ormore terms of the term vector each have one or more respective weightsthat exceed a defined threshold; identifying targeting data for acontent item that is available for presentation with various differentresources; determining that the content item is relevant to the targetresource, the determining including: comparing the identified targetingdata for the content item to the determined one or more terms of theterm vector for the target resource; and determining, based on thecomparison, that the identified targeting data for the content item andthe determined one or more terms of the term vector have a degree ofsimilarity that is greater than a similarity threshold; and serving thecontent item based on determining that the identified targeting data forthe content item has the degree of similarity to the determined one ormore terms of the term vector for the target resource.
 2. The computerimplemented method of claim 1, wherein the identified targeting data isprovided by a content provider for the content item and comprises atleast one of (A) a keyword and (B) a phrase, and wherein the identifiedtargeting data for the content item is received via a content itemcampaign entry and management component of a server system.
 3. Thecomputer implemented method of claim 1, further comprising determiningone or more topics that correspond to the target resource by determiningthat one or more terms of the term vector each have one or morerespective weights that exceed a defined threshold.
 4. The computerimplemented method of claim 3, wherein the one or more topicscorresponding to the target resource comprise at least one topic fromanother resource linked to the target resource.
 5. The computerimplemented method of claim 3, wherein the one or more topicscorresponding to the target resource comprise anchor text in a link fromanother resource to the target resource.
 6. The computer implementedmethod of claim 3, wherein the one or more topics corresponding to thetarget resource comprise text from queries to a search engine thatreturned a search result comprising the target resource.
 7. The computerimplemented method of claim 3, wherein the one or more topicscorresponding to the target resource comprise text from queries to asearch engine, wherein the search engine is configured to return asearch result including the target resource, the search result beingsubsequently selectable by a user.
 8. A non-transitory computer readablemedium storing instructions that are executable by one or moreprocessors to perform operations comprising: obtaining a set of rulesthat specify weights for each term of a plurality of terms extractedfrom a resource, with a weight of a term being a function of a frequencyof the term in the resource; extracting a plurality of terms from atarget resource; generating, based on the set of rules, a term vectorthat represents weights for the plurality of terms that appear withinthe target resource; determining that one or more terms of the termvector each have one or more respective weights that exceed a definedthreshold; identifying targeting data for a content item that isavailable for presentation with various different resources; determiningthat the content item is relevant to the target resource, thedetermining including: comparing the identified targeting data for thecontent item to the determined one or more terms of the term vector forthe target resource; and determining, based on the comparison, that theidentified targeting data for the content item and the determined one ormore terms of the term vector have a degree of similarity that isgreater than a similarity threshold; and serving the content item basedon determining that the identified targeting data for the content itemhas the degree of similarity to the determined one or more terms of theterm vector for the target resource.
 9. The non-transitory computerreadable medium of claim 8, wherein the identified targeting data isprovided by a content provider for the content item and comprises atleast one of (A) a keyword and (B) a phrase, and wherein the identifiedtargeting data for the content item is received via a content itemcampaign entry and management component of a server system.
 10. Thenon-transitory computer readable medium of claim 8, the operationsfurther comprising determining one or more topics that correspond to thetarget resource by determining that one or more terms of the term vectoreach have one or more respective weights that exceed a definedthreshold.
 11. The non-transitory computer readable medium of claim 10,wherein the one or more topics corresponding to the target resourcefurther comprise at least one topic from another resource linked to thetarget resource.
 12. The non-transitory computer readable medium ofclaim 10, wherein the one or more topics corresponding to the targetresource further comprise anchor text in a link from another resource tothe target resource.
 13. The non-transitory computer readable medium ofclaim 10, wherein the one or more topics corresponding to the targetresource further comprise text from queries to a search engine thatreturned a search result including the target resource.
 14. Thenon-transitory computer readable medium of claim 10, wherein the one ormore topics corresponding to the target resource further comprise textfrom queries to a search engine, wherein the search engine returns asearch result including the target resource, the search result beingsubsequently selectable by a user.
 15. A computer implemented methodcomprising: obtaining a set of rules that specify weights for each termof a plurality of terms extracted from a resource, with a weight of aterm being a function of a frequency of the term in the resource;extracting a plurality of terms from a target resource; generating,based on the set of rules, a term vector that represents weights for theplurality of terms that appear within text of the target resource,wherein the weights are based on at least one of (A) whether the termappears in the text of the target resource more frequently, relative toone or more other terms that each appear less frequently, and (B)whether the term appears less frequently, relative to one or more otherterms that each appear more frequently, across a collection of resourcesto which the target resource belongs; identifying targeting data for acontent item that is available for presentation with various differentresources; determining that the content item is relevant to the targetresource, the determining including: comparing the identified targetingdata for the content item to the determined one or more terms of theterm vector for the target resource; and determining, based on thecomparison, that the identified targeting data for the content item andthe determined one or more terms of the term vector have a degree ofsimilarity that is greater than a similarity threshold; and serving thecontent item based on determining that the identified targeting data forthe content item has the degree of similarity to the determined one ormore terms of the term vector for the target resource.
 16. The computerimplemented method of claim 15, further comprising determining one ormore topics that correspond to the target resource by determining thatone or more terms of the term vector each have one or more respectiveweights that exceed a defined threshold.
 17. The computer implementedmethod of claim 16, wherein comparing comprises scoring a similaritybetween the identified targeting data for the content item and the oneor more topics that correspond to the target resource and determiningthat the content item is relevant to the target resource when thesimilarity is above a threshold score.
 18. The computer implementedmethod of claim 15, wherein the identified targeting data is provided bya content provider for the content item and comprises at least one of(A) a keyword and (B) a phrase, and wherein the identified targetingdata for the content item is received via a content item campaign entryand management component of a server system.
 19. A system comprising:one or more processing devices; and at least one non transitory computerreadable medium storing instructions operable to cause the one or moreprocessing devices to perform operations comprising: obtaining a set ofrules that specify weights for each term of a plurality of termsextracted from a resource, with a weight of a term being a function of afrequency of the term in the resource; extracting a plurality of termsfrom a target resource; generating, based on the set of rules, a termvector that represents weights for the plurality of terms that appearwithin the target resource; determining that one or more terms of theterm vector each have one or more respective weights that exceed adefined threshold; identifying targeting data for a content item that isavailable for presentation with various different resources; determiningthat the content item is relevant to the target resource, thedetermining including: comparing the identified targeting data for thecontent item to the determined one or more terms of the term vector forthe target resource; and determining, based on the comparison, that theidentified targeting data for the content item and the determined one ormore terms of the term vector have a degree of similarity that isgreater than a similarity threshold; and serving the content item basedon determining that the identified targeting data for the content itemhas the degree of similarity to the determined one or more terms of theterm vector for the target resource.
 20. The system of claim 19, whereinthe operations further comprise: determining one or more topics thatcorrespond to the target resource by determining that one or more termsof the term vector each have one or more respective weights that exceeda defined threshold.